Quantum Simulations with Physics-Informed Neural Networks

Physics-Informed Neural Networks (PINNs) have emerged as a powerful tool for solving PDEs, fractional equations, and eigenvalue problems, making them particularly suited for quantum mechanics, where traditional numerical methods often face computational bottlenecks. The aim of this internship is to analyse the applicability of PINNs to a broad range of quantum simulation tasks such as solving the Schrödinger equation for multi-electron systems or simulating molecular dynamics for processes like silicidation. The intern will engage in hands-on scientific machine learning, contribute to improvements in quantum simulation, and acquire valuable skills in a rapidly evolving field.

Faculty Supervisor:

David Cooke

Student:

Partner:

Fraunhofer-Institut für Integrierte Systeme und Bauelementetechnologie

Discipline:

Physics

Sector:

Quantum Science; Technology

University:

McGill University

Program: